Bandwidth is becoming commoditized and markets are starting to appear. Potential behaviors of these markets are not understood because these markets are still in the early stages of development. This is reflected in the lack of current research on the structure and dynamics of network commodity market prices. A method is presented for constructing telecom commodity spot price processes. Bandwidth, like electricity, is not storable, so inspiration is drawn from electricity prices and models. However, unique network features of telecommunications require specific inclusion. These are: geographical substitution, referred to as arbitrage; quality of service (QoS); and the continuing pace of technological development. Developing liquidity acts as a further complication. Liquidity refers to the ease with which partners for trades at a given price can be found. Geographical arbitrage means that spot price development on point-to-point links cannot be understood in isolation from price development on alternative paths with equivalent QoS. This implies some form of price modification derived from load-balancing across appropriately specified QoS limited alternative paths. Technological development continually pushes prices down as new equipment is installed by competitors. So, unlike other commodities, the prices revert towards a mean whose drift is strongly structurally downwards. Market liquidity is quantified as the extent to which geographical arbitrage modifies link-prices. Thus price development is modeled as a combination of link-price processes modified by prices for equivalent QoS paths. The presented model covers the existence and value of arbitrage opportunities together with its effect on price development and network present-value (NPV). Application of this work ranges from network design to infrastructure valuation and construction of real options. 1998, McGraw-Hill, New York, a pricing model for energy markets is described which is very similar to a model for general commodities which was exactly equivalent to an earlier model under a linear transformation of parameters, described in “The stochastic behavior of commodity prices: implications for valuing and hedging”, E. Schwartz. 1997, J. Finance 52, pp. 923-973, The observations of congestion on the Internet suggest however, that even for single links, these models are insufficient because they do not include spikes or jumps in prices. Price spikes are particular features of electricity prices and some modelling has been done there as described in “Stochastic models of energy commodity prices and their applications: mean-reversion with jumps and spikes”, S. Deng, 1998. PSERC working paper 98-28, readable at http://www.pserc.wisc.edu/.
The international publication WO 00/54198A2 refers to a system, method, software, and portfolios for managing risk in markets relating to a commodity delivered over a network, in which a market participant constructs portfolios of preferably liquid price risk instruments in proportions that eliminate the Spatial Price Risk for the market participant's underlying position. Techniques are also disclosed for constructing and evaluating new price risk instruments and other sets of positions, as well as identifying arbitrage opportunities in those markets.
Apart from link-price processes in isolation there is the question of network effects in the sense of interactions. The most important network effect here is geographical arbitrage, i.e. the existence of many prices for end-to-end capacity at equivalent QoS. This has been observed in the market. Detection of such arbitrage opportunities is in general an NP complete problem based on shortest path algorithms with side constraints. A variety of pseudo-polynomial time algorithms exist that result from quantification of these constraints as is to be expected under commoditization. Geographical arbitrage opportunities could exist in the forward market even with a no-arbitrage situation in the spot market. There is vast computer science literature on price-setting for network resources in order to achieve some aim, e.g. social welfare maximization, cost-allocation, congestion control, etc. Here price dynamics are approached from a completely different direction in that it is started from modelling the price process rather than modelling supply and demand and then solving for the best price in some sense relative to a given network.